A randomized algorithm for the decomposition of matrices

Given an m × n matrix A and a positive integer k, we describe a randomized procedure for the approximation of A with a matrix Z of rank k. The procedure relies on applying A T to a collection of l random vectors, where l is an integer equal to or slightly greater than k; the scheme is efficient when...

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Bibliographic Details
Published in:Applied and computational harmonic analysis Vol. 30; no. 1; pp. 47 - 68
Main Authors: Martinsson, Per-Gunnar, Rokhlin, Vladimir, Tygert, Mark
Format: Journal Article
Language:English
Published: Elsevier Inc 2011
Subjects:
Algorithm
Algorithms
Approximation
Decomposition
Estimates
Integers
Lanczos
Low rank
Mathematical analysis
Matrix
Randomized
SVD
Vectors (mathematics)
SVD
Lanczos
Randomized
Matrix
Algorithm
Low rank
ISSN:1063-5203, 1096-603X
Online Access:Get full text
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Summary:Given an m × n matrix A and a positive integer k, we describe a randomized procedure for the approximation of A with a matrix Z of rank k. The procedure relies on applying A T to a collection of l random vectors, where l is an integer equal to or slightly greater than k; the scheme is efficient whenever A and A T can be applied rapidly to arbitrary vectors. The discrepancy between A and Z is of the same order as l m times the ( k + 1 ) st greatest singular value σ k + 1 of A, with negligible probability of even moderately large deviations. The actual estimates derived in the paper are fairly complicated, but are simpler when l − k is a fixed small nonnegative integer. For example, according to one of our estimates for l − k = 20 , the probability that the spectral norm ‖ A − Z ‖ is greater than 10 ( k + 20 ) m σ k + 1 is less than 10 − 17 . The paper contains a number of estimates for ‖ A − Z ‖ , including several that are stronger (but more detailed) than the preceding example; some of the estimates are effectively independent of m. Thus, given a matrix A of limited numerical rank, such that both A and A T can be applied rapidly to arbitrary vectors, the scheme provides a simple, efficient means for constructing an accurate approximation to a singular value decomposition of A. Furthermore, the algorithm presented here operates reliably independently of the structure of the matrix A. The results are illustrated via several numerical examples.
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ISSN:1063-5203
1096-603X
DOI:10.1016/j.acha.2010.02.003